Invert4TVG: A Temporal Video Grounding Framework with Inversion Tasks Preserving Action Understanding Ability

The paper introduces Invert4TVG, a temporal video grounding framework that adds inversion tasks to preserve action understanding. It defines three inversion tasks—Verb Completion, Action Recognition, and Video Description—derived from existing TVG annotations and integrates them via reinforcement learning. Experiments demonstrate a 7.1% improvement in R1@0.7 on Charades-STA for a 3B model, outperforming state‑of‑the‑art methods.

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• reinforcement-learning
• benchmarks